9 results on '"Triantafyllidis, Andreas"'
Search Results
2. Effectiveness, reach, uptake, and feasibility of digital health interventions for adults with hypertension: a systematic review and meta-analysis of randomised controlled trials
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Siopis, George, Moschonis, George, Eweka, Evette, Jung, Jenny, Kwasnicka, Dominika, Asare, Bernard Yeboah-Asiamah, Kodithuwakku, Vimarsha, Willems, Ruben, Verhaeghe, Nick, Annemans, Lieven, Vedanthan, Rajesh, Oldenburg, Brian, Manios, Yannis, Oldenburg, Brian, Kwasnicka, Dominika, Gong, Enying, Jung, Jenny, Asare, Bernard Yeboah-Asiamah, Kodithuwakku, Vimarsha, Votis, Konstantinos, Segkouli, Sofia, Triantafyllidis, Andreas, Kyparissis, Odysseas, Paliokas, Ioannis, Polychroniou, Eleftheria, Annemans, Lieven, Verhaeghe, Nick, Willems, Ruben, De Craemer, Dirk, Manios, Yannis, Anastasiou, Kostas, Tserpes, Konstantinos, Mavrogianni, Christina, Karaglani, Eva, Kalogerakou, Electra, Maragkoudaki, Maria, Ntzouvani, Agathi, Kontochristopoulou, Katerina, Dupont, Sabine, Dupont, Elizabeth, Dauzon, Leo, Roskams, Maartje, Lennox-Chhugani, Niamh, Perrin, Martin, Day, Niamh Daly, Ferrer, Georgina, Snook, Orla, Aldasoro, Edelweiss, Gil-Salmerón, Alejandro, Peiró, Pilar Gangas, Curran, Darren, Lyne, Fiona, Curreri, Nereide A., Moschonis, George, Siopis, George, Pierantozzi, Nazzareno, D'Antonio, Claudia, Vespasiani, Giacomo, Almonti, Teresa, Skouteris, Helen, Taylor, Tracy, Savaglio, Melissa, Makrilakis, Konstantinos, Stergiou, George, Liatis, Stavros, Karamanakos, George, Koliaki, Chrysi, Kollias, Anastasios, Zikou, Eva, Dimosthenopoulos, Haris, Vedanthan, Rajesh, Huang, Keng-Yen, Adhikari, Samrachana, Qian, Kun, Dickhaus, Julia, Carney, Kimberly, Eweka, Evette, Sahito, Farhan, Pavlovic, Dusan, Djokic, Djordje, Sahito, Arzoo, Battalova, Gisella, Seghieri, Chiara, Nutti, Sabina, Vanieri, Milena, Belle, Nicola, Bertarelli, Gaia, Cantarelli, Paola, Ferre, Francesca, Noci, Anna, Tortu, Constanza, Bozzi, Nadia, Ferrari, Dina, Borelli, Rachele, Iotova, Violeta, Yotov, Yoto, Usheva, Natalia, Kozhuharova, Anna, Russeva, Vanya, Marinova, Vanya, Koleva, Sonya, Atanasova, Virginia, Stefanova, Tanya, Tsochev, Kaloyan, Aznar, Luis Moreno, Botaya, Rosa Magallón, Lozano, Gloria Bueno, De Miguel-Etayo, Pilar, Gonzalez-Gil, Esther Ma, Miguel-Berges, María L., Pérez, Susana, Blázquez, Bárbara Oliván, Giménez-Legarre, Natalia, Toti, Florian, Prifti, Skerdi, Bombaj, Blerina, Doracaj, Ditila, Laze, Ornela, Lapardhaja, Adriana, and Bruka, Luftime
- Abstract
Digital health interventions are effective for hypertension self-management, but a comparison of the effectiveness and implementation of the different modes of interventions is not currently available. This study aimed to compare the effectiveness of SMS, smartphone application, and website interventions on improving blood pressure in adults with hypertension, and to report on their reach, uptake, and feasibility.
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- 2023
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3. Effectiveness, reach, uptake, and feasibility of digital health interventions for adults with type 2 diabetes: a systematic review and meta-analysis of randomised controlled trials
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Moschonis, George, Siopis, George, Jung, Jenny, Eweka, Evette, Willems, Ruben, Kwasnicka, Dominika, Asare, Bernard Yeboah-Asiamah, Kodithuwakku, Vimarsha, Verhaeghe, Nick, Vedanthan, Rajesh, Annemans, Lieven, Oldenburg, Brian, Manios, Yannis, Oldenburg, Brian, Kwasnicka, Dominika, Gong, Enying, Jung, Jenny, Asare, Bernard Yeboah-Asiamah, Kodithuwakku, Vimarsha, Votis, Konstantinos, Segkouli, Sofia, Triantafyllidis, Andreas, Kyparissis, Odysseas, Paliokas, Ioannis, Polychroniou, Eleftheria, Annemans, Lieven, Verhaeghe, Nick, Willems, Ruben, De Craemer, Dirk, Manios, Yannis, Anastasiou, Kostas, Tserpes, Konstantinos, Mavrogianni, Christina, Karaglani, Eva, Kalogerakou, Electra, Maragkoudaki, Maria, Ntzouvani, Agathi, Kontochristopoulou, Katerina, Dupont, Sabine, Dupont, Elizabeth, Dauzon, Leo, Roskams, Maartje, Lennox-Chhugani, Niamh, Perrin, Martin, Day, Niamh Daly, Ferrer, Georgina, Snook, Orla, Aldasoro, Edelweiss, Gil-Salmerón, Alejandro, Peiró, Pilar Gangas, Curran, Darren, Lyne, Fiona, Curreri, Nereide A., Moschonis, George, Siopis, George, Pierantozzi, Nazzareno, D'Antonio, Claudia, Vespasiani, Giacomo, Almonti, Teresa, Skouteris, Helen, Taylor, Tracy, Savaglio, Melissa, Makrilakis, Konstantinos, Stergiou, George, Liatis, Stavros, Karamanakos, George, Koliaki, Chrysi, Kollias, Anastasios, Zikou, Eva, Dimosthenopoulos, Haris, Vedanthan, Rajesh, Huang, Keng-Yen, Adhikari, Samrachana, Qian, Kun, Dickhaus, Julia, Carney, Kimberly, Eweka, Evette, Sahito, Farhan, Pavlovic, Dusan, Djokic, Djordje, Sahito, Arzoo, Battalova, Gisella, Seghieri, Chiara, Nutti, Sabina, Vanieri, Milena, Belle, Nicola, Bertarelli, Gaia, Cantarelli, Paola, Ferre, Francesca, Noci, Anna, Tortu, Constanza, Bozzi, Nadia, Ferrari, Dina, Borelli, Rachele, Iotova, Violeta, Yotov, Yoto, Usheva, Natalia, Kozhuharova, Anna, Russeva, Vanya, Marinova, Vanya, Koleva, Sonya, Atanasova, Virginia, Stefanova, Tanya, Tsochev, Kaloyan, Aznar, Luis Moreno, Botaya, Rosa Magallón, Lozano, Gloria Bueno, De Miguel-Etayo, Pilar, Gonzalez-Gil, Esther Ma, Miguel-Berges, María L., Pérez, Susana, Blázquez, Bárbara Oliván, Giménez-Legarre, Natalia, Toti, Florian, Prifti, Skerdi, Bombaj, Blerina, Doracaj, Ditila, Laze, Ornela, Lapardhaja, Adriana, and Bruka, Luftime
- Abstract
Digital health interventions have shown promising results for the management of type 2 diabetes, but a comparison of the effectiveness and implementation of the different modes is not currently available. Therefore, this study aimed to compare the effectiveness of SMS, smartphone application, and website-based interventions on improving glycaemia in adults with type 2 diabetes and report on their reach, uptake, and feasibility.
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- 2023
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4. Guest Editorial Pervasive Computing in Healthcare
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Tsanas, Athanasios, Triantafyllidis, Andreas, and Tsiknakis, Manolis
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Pervasive computing has revolutionized how we collect data and interact with information. Research interest in pervasive computing has been growing exponentially over the years, demonstrating enormous potential in biomedical applications ranging from a research-fertile field to clinical translation and healthcare delivery [1]. The sophisticated capabilities of smartphones integrating diverse sensors along with wearable and non-wearable sensors provide the opportunity to collect longitudinal, multimodal data streams and facilitate near real-time monitoring, over and above standardized self-reports [1], [2], [3], [4]. Overall, digital technologies (e.g., smartphones and smartwatches) are becoming increasingly affordable and have already been embraced by many people including elders [2], [3], facilitating large scale investigations and clinical trials. Furthermore, ubiquitous devices such as standard telephones, for example, have been used to collect speech signals for healthcare assessments, enabling large studies (∼10000 people) within months, across multiple countries, with minimal cost [5]. Nation-wide studies reaching 100000+ people who contribute their data have become possible, such as the U.K. BioBank (
https://www.ukbiobank.ac.uk/ ), which has enabled novel data explorations into incident cardiovascular disease at scale [6]. Many innovative solutions capitalizing on (large) data streams from different sources have been proposed, coupled with emerging advances in data science and machine learning which enable fast and advanced processing of the collected datasets [7].- Published
- 2024
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5. The development and codesign of the PATHway intervention: a theory-driven eHealth platform for the self-management of cardiovascular disease
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Walsh, Deirdre M J, Moran, Kieran, Cornelissen, Veronique, Buys, Roselien, Claes, Jomme, Zampognaro, Paolo, Melillo, Fabio, Maglaveras, Nicos, Chouvarda, Ioanna, Triantafyllidis, Andreas, Filos, Dimitris, and Woods, Catherine B
- Abstract
The development of technology-based interventions for people with cardiovascular disease needs to be reported clearly throughout each development stage. This kind of reporting will allow future research to repeat best practice development and can aid future evaluation.Cardiovascular diseases (CVDs) are a leading cause of premature death worldwide. International guidelines recommend routine delivery of all phases of cardiac rehabilitation (CR). Uptake of traditional CR remains suboptimal, as attendance at formal hospital-based CR programs is low, with community-based CR rates and individual long-term exercise maintenance even lower. Home-based CR programs have been shown to be equally effective in clinical and health-related quality of life outcomes and yet are not readily available. The aim of the current study was to develop the PATHway intervention (physical activity toward health) for the self-management of CVD. Increasing physical activity in individuals with CVD was the primary behavior. The PATHway intervention was theoretically informed by the behavior change wheel and social cognitive theory. All relevant intervention functions, behavior change techniques, and policy categories were identified and translated into intervention content. Furthermore, a person-centered approach was adopted involving an iterative codesign process and extensive user testing. Education, enablement, modeling, persuasion, training, and social restructuring were selected as appropriate intervention functions. Twenty-two behavior change techniques, linked to the six intervention functions and three policy categories, were identified for inclusion and translated into PATHway intervention content. This paper details the use of the behavior change wheel and social cognitive theory to develop an eHealth intervention for the self-management of CVD. The systematic and transparent development of the PATHway intervention will facilitate the evaluation of intervention effectiveness and future replication.
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- 2019
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6. Social Robot Interventions for Child Healthcare: A Systematic Review of the Literature
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Triantafyllidis, Andreas, Alexiadis, Anastasios, Votis, Konstantinos, and Tzovaras, Dimitrios
- Abstract
•Social robots can promote healthy behaviors in child healthcare•Social robots should be considered in the design of psychological interventions•Rigorous studies with social robot-based interventions are needed
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- 2023
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7. COVID-19 Mobile Apps: A Systematic Review of the Literature.
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Kondylakis, Haridimos, Katehakis, Dimitrios G, Kouroubali, Angelina, Logothetidis, Fokion, Triantafyllidis, Andreas, Kalamaras, Ilias, Votis, Konstantinos, and Tzovaras, Dimitrios
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COVID-19 ,MOBILE apps ,COVID-19 pandemic ,BIBLIOGRAPHIC databases ,SCIENTIFIC literature ,PANDEMICS - Abstract
Background: A vast amount of mobile apps have been developed during the past few months in an attempt to "flatten the curve" of the increasing number of COVID-19 cases.Objective: This systematic review aims to shed light into studies found in the scientific literature that have used and evaluated mobile apps for the prevention, management, treatment, or follow-up of COVID-19.Methods: We searched the bibliographic databases Global Literature on Coronavirus Disease, PubMed, and Scopus to identify papers focusing on mobile apps for COVID-19 that show evidence of their real-life use and have been developed involving clinical professionals in their design or validation.Results: Mobile apps have been implemented for training, information sharing, risk assessment, self-management of symptoms, contact tracing, home monitoring, and decision making, rapidly offering effective and usable tools for managing the COVID-19 pandemic.Conclusions: Mobile apps are considered to be a valuable tool for citizens, health professionals, and decision makers in facing critical challenges imposed by the pandemic, such as reducing the burden on hospitals, providing access to credible information, tracking the symptoms and mental health of individuals, and discovering new predictors. [ABSTRACT FROM AUTHOR]- Published
- 2020
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8. Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature.
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Triantafyllidis, Andreas K and Tsanas, Athanasios
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MACHINE learning ,TELEMEDICINE ,ARTIFICIAL intelligence in medicine ,DATA mining ,STRESS management - Abstract
Background: Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals.Objective: Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain.Methods: We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction).Results: Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes.Conclusions: This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice. [ABSTRACT FROM AUTHOR]- Published
- 2019
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9. A user-centred home monitoring and self-management system for patients with heart failure: a multicentre cohort study
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Rahimi, Kazem, Velardo, Carmelo, Triantafyllidis, Andreas, Conrad, Nathalie, Shah, Syed Ahmar, Chantler, Tracey, Mohseni, Hamid, Stoppani, Emma, Moore, Francesca, Paton, Chris, Emdin, Connor A., Ernst, Johanna, Tarassenko, Lionel, Investigators, On behalf of the SUPPORT-HF, Investigators, On behalf of the SUPPORT-HF, Rahimi, Kazem, Velardo, Carmelo, Triantafyllidis, Andreas, Conrad, Nathalie, Ahmar Shah, Syed, Chantler, Tracey, Mohseni, Hamid, Stoppani, Emma, Moore, Francesca, Paton, Chris, Tarassenko, Lionel, Cleland, John, Emptage, Felicity, Chantler, Tracey, Farmer, Andrew, Fitzpatrick, Raymond, Hobbs, Richard, MacMahon, Stephen, Perkins, Alan, Rahimi, Kazem, Tarassenko, Lionel, Altmann, Paul, Chandrasekaran, Badri, Emdin, Connor A., Ernst, Johanna, Foley, Paul, Hersch, Fred, Salimi-Khorshidi, Gholamreza, Noble, Joanne, and Woodward, Mark
- Abstract
Aims Previous generations of home monitoring systems have had limited usability. We aimed to develop and evaluate a user-centred and adaptive system for health monitoring and self-management support in patients with heart failure.Methods and results Patients with heart failure were recruited from three UK centres and provided with Internet-enabled tablet computers that were wirelessly linked with sensor devices for blood pressure, heart rate, and weight monitoring. Patient observations, interviews, and concurrent analyses of the automatically collected data from their monitoring devices were used to increase the usability of the system. Of the 52 participants (median age 77 years, median follow-up 6 months [interquartile range, IQR, 3.6–9.2]), 24 (46%) had no, or very limited prior, experience with digital technologies. It took participants about 1.5 min to complete the daily monitoring tasks, and the rate of failed attempts in completing tasks was <5%. After 45 weeks of observation, participants still used the system on 4.5 days per week (confidence interval 3.2–5.7 days). Of the 46 patients who could complete the final survey, 93% considered the monitoring system as easy to use and 38% asked to keep the system for self-management support after the study was completed.Conclusion We developed a user-centred home monitoring system that enabled a wide range of heart failure patients, with differing degrees of IT literacy, to monitor their health status regularly. Despite no active medical intervention, patients felt that they benefited from the reassurance and sense of connectivity that the monitoring system provided.- Published
- 2015
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